Purpose This study investigates the implications of all degrees of freedom of within‐scan patient head motion on patient safety. Methods Electromagnetic simulations were performed by displacing and/or rotating a virtual body model inside an 8‐channel transmit array to simulate 6 degrees of freedom of motion. Rotations of up to 20° and displacements of up to 20 mm including off‐axis axial/coronal translations were investigated, yielding 104 head positions. Quadrature excitation, RF shimming, and multi‐spoke parallel‐transmit excitation pulses were designed for axial slice‐selection at 7T, for seven slices across the head. Variation of whole‐head specific absorption rate (SAR) and 10‐g averaged local SAR of the designed pulses, as well as the change in the maximum eigenvalue (worst‐case pulse) were investigated by comparing off‐center positions to the central position. Results In their respective worst‐cases, patient motion increased the eigenvalue‐based local SAR by 42%, whole‐head SAR by 60%, and the 10‐g averaged local SAR by 210%. Local SAR was observed to be more sensitive to displacements along right–left and anterior–posterior directions than displacement in the superior–inferior direction and rotation. Conclusion This is the first study to investigate the effect of all 6 degrees of freedom of motion on safety of practical pulses. Although the results agree with the literature for overlapping cases, the results demonstrate higher increases (up to 3.1‐fold) in local SAR for off‐axis displacement in the axial plane, which had received less attention in the literature. This increase in local SAR could potentially affect the local SAR compliance of subjects, unless realistic within‐scan patient motion is taken into account during pulse design.

Tailored parallel-transmit (pTx) pulses produce uniform excitation profiles at 7 T, but are sensitive to head motion. A potential solution is real-time pulse redesign. A deep learning framework is proposed to estimate pTx B + 1 distributions following within-slice motion, which can then be used for tailored pTx pulse redesign.Methods: Using simulated data, conditional generative adversarial networks were trained to predict B + 1 distributions in the head following a displacement. Predictions were made for two virtual body models that were not included in training. Predicted maps were compared with ground-truth (simulated, following motion) B 1 maps. Tailored pTx pulses were designed using B 1 maps at the original position (simulated, no motion) and evaluated using simulated B 1 maps at displaced position (ground-truth maps) to quantify motion-related excitation error.A second pulse was designed using predicted maps (also evaluated on groundtruth maps) to investigate improvement offered by the proposed method. Results: Predicted B + 1 maps corresponded well with ground-truth maps. Error in predicted maps was lower than motion-related error in 99% and 67% of magnitude and phase evaluations, respectively. Worst-case flip-angle normalized RMS error due to motion (76% of target flip angle) was reduced by 59% when pulses were redesigned using predicted maps. Conclusion:We propose a framework for predicting B + 1 maps online with deep neural networks. Predicted maps can then be used for real-time tailored pulse redesign, helping to overcome head motion-related error in pTx.

A central limitation of multiple-acquisition magnetic resonance imaging (MRI) is the degradation in scan efficiency as the number of distinct datasets grows. Sparse recovery techniques can alleviate this limitation via randomly undersampled acquisitions. A frequent sampling strategy is to prescribe for each acquisition a different random pattern drawn from a common sampling density. However, naive random patterns often contain gaps or clusters across the acquisition dimension that in turn can degrade reconstruction quality or reduce scan efficiency. To address this problem, a statistically-segregated sampling method is proposed for multiple-acquisition MRI. This method generates multiple patterns sequentially, while adaptively modifying the sampling density to minimize k-space overlap across patterns. As a result, it improves incoherence across acquisitions while still maintaining similar sampling density across the radial dimension of k-space. Comprehensive simulations and in vivo results are presented for phase-cycled balanced steady-state free precession and multi-echo T 2 -weighted imaging. Segregated sampling achieves significantly improved quality in both Fourier and compressedsensing reconstructions of multiple-acquisition datasets.

Multi‐contrast images are commonly acquired together to maximize complementary diagnostic information, albeit at the expense of longer scan times. A time‐efficient strategy to acquire high‐quality multi‐contrast images is to accelerate individual sequences and then reconstruct undersampled data with joint regularization terms that leverage common information across contrasts. However, these terms can cause features that are unique to a subset of contrasts to leak into the other contrasts. Such leakage‐of‐features may appear as artificial tissues, thereby misleading diagnosis. The goal of this study is to develop a compressive sensing method for multi‐channel multi‐contrast magnetic resonance imaging (MRI) that optimally utilizes shared information while preventing feature leakage. Joint regularization terms group sparsity and colour total variation are used to exploit common features across images while individual sparsity and total variation are also used to prevent leakage of distinct features across contrasts. The multi‐channel multi‐contrast reconstruction problem is solved via a fast algorithm based on Alternating Direction Method of Multipliers. The proposed method is compared against using only individual and only joint regularization terms in reconstruction. Comparisons were performed on single‐channel simulated and multi‐channel in‐vivo datasets in terms of reconstruction quality and neuroradiologist reader scores. The proposed method demonstrates rapid convergence and improved image quality for both simulated and in‐vivo datasets. Furthermore, while reconstructions that solely use joint regularization terms are prone to leakage‐of‐features, the proposed method reliably avoids leakage via simultaneous use of joint and individual terms, thereby holding great promise for clinical use.

The ultimate intrinsic signal-to-noise ratio is the highest possible signal-to-noise ratio, and the ultimate intrinsic specific absorption rate provides the lowest limit of the specific absorption rate for a given flip angle distribution. Analytic expressions for ultimate intrinsic signal-to-noise ratio and ultimate intrinsic specific absorption rate are obtained for arbitrary sample geometries. These expressions are valid when the distance between the point of interest and the sample surface is smaller than the wavelength, and the sample is homogeneous. The dependence on the sample permittivity, conductivity, temperature, size, and the static magnetic field strength is given in analytic form, which enables the easy evaluation of the change in signal-to-noise ratio and specific absorption rate when the sample is scaled in size or when any of its geometrical or electrical parameters is altered. Furthermore, it is shown that signal-to-noise ratio and specific absorption rate are independent of the permeability of the sample. As a practical case and a solution example, a uniform, circular cylindrically shaped sample is studied.

The specific absorption rate is used as one of the main safety parameters in magnetic resonance imaging. The performance of imaging sequences is frequently hampered by the limitations imposed on the specific absorption rate that increase in severity at higher field strengths. The most well-known approach to reducing the specific absorption rate is presumably the variable rate selective excitation technique, which modifies the gradient waveforms in time. In this article, an alternative approach is introduced that uses gradient fields with nonlinear variations in space to reduce the specific absorption rate. The effect of such gradient fields on the relationship between the desired excitation profile and the corresponding radiofrequency pulse is shown. The feasibility of the method is demonstrated using three examples of radiofrequency pulse design. Finally, the proposed method is compared with and combined with the variable rate selective excitation technique. Magn Reson Med 70:537-546, 2013.

Purpose An iterative k‐space trajectory and radiofrequency (RF) pulse design method is proposed for excitation using nonlinear gradient magnetic fields. Theory and Methods The spatial encoding functions (SEFs) generated by nonlinear gradient fields are linearly dependent in Cartesian coordinates. Left uncorrected, this may lead to flip angle variations in excitation profiles. In the proposed method, SEFs (k‐space samples) are selected using a matching pursuit algorithm, and the RF pulse is designed using a conjugate gradient algorithm. Three variants of the proposed approach are given: the full algorithm, a computationally cheaper version, and a third version for designing spoke‐based trajectories. The method is demonstrated for various target excitation profiles using simulations and phantom experiments. Results The method is compared with other iterative (matching pursuit and conjugate gradient) and noniterative (coordinate‐transformation and Jacobian‐based) pulse design methods as well as uniform density spiral and EPI trajectories. The results show that the proposed method can increase excitation fidelity. Conclusion An iterative method for designing k‐space trajectories and RF pulses using nonlinear gradient fields is proposed. The method can either be used for selecting the SEFs individually to guide trajectory design, or can be adapted to design and optimize specific trajectories of interest. Magn Reson Med 74:826–839, 2015. © 2014 Wiley Periodicals, Inc.

No abstract